Epigenome-wide association studies (EWAS) and differential gene expression analyses are generally performed on tissue samples, which consist of multiple cell types. Cell-type-specific effects of a trait, such as disease, on the omics expression are of interest but difficult or costly to measure experimentally. By measuring omics data for the bulk tissue, cell type composition of a sample can be inferred statistically. Subsequently, cell-type-specific effects are estimated by linear regression that includes terms representing the interaction between the cell type proportions and the trait. This approach involves two issues, scaling and multicollinearity.First, although cell composition is analyzed in linear scale, differential methylation/expression is analyzed suitably in the logit/log scale. To simultaneously analyze two scales, we applied nonlinear regression. Second, we show that the interaction terms are highly collinear, which is obstructive to ordinary regression. To cope with the multicollinearity, we applied ridge regularization. In simulated data, nonlinear ridge regression attained well-balanced sensitivity, specificity and precision. Marginal model attained the lowest precision and highest sensitivity and was the only algorithm to detect weak signal in real data.https://github.com/fumi-github/omicwasNonlinear ridge regression performed cell-type-specific association test on bulk omics data with well-balanced performance. The omicwas package for R implements nonlinear ridge regression for cell-type-specific EWAS, differential gene expression and QTL analyses. The software is freely available from The online version contains supplementary material available at 10.1186/s12859-021-03982-3. Epigenome-wide association studies (EWAS) and differential gene expression analyses elucidate the association of disease traits (or conditions) with the level of omics expression, namely DNA methylation and gene expression. Thus far, tissue samples, which consist of heterogeneous cell types, have mainly been examined, because cell sorting is not feasible in most tissues and single-cell assay is still expensive. Nevertheless, the cell type composition of a sample can be quantified statistically by comparing omics measurement of the target sample with reference data obtained from sorted or single cells , 2. By uFor the imputation of cell type composition, omics markers are usually analyzed in the original linear scale, which measures the proportion of mRNA molecules from a specific gene or the proportion of methylated cytosine molecules among all cytosines at a specific CpG site . The proAiming to perform cell-type-specific EWAS or differential gene expression analyses by using unsorted tissue samples, we study two issues that have been overlooked. Whereas previous studies were performed in linear scale, we develop a nonlinear regression, which simultaneously analyzes cell type composition in linear scale and differential expression/methylation in log/logit scale. The second issue is multicollinearity. Cell-type-specific effects of a trait, such as disease, on omics expression are usually estimated by linear regression that includes terms representing the interaction between the cell type proportions and the trait. We show that the interaction terms can mutually be highly correlated, which obstructs ordinary regression. To cope with the multicollinearity, we implement ridge regularization. Our methods and previous ones are compared in simulated and real data.k on the expression level in cell type h. This is estimated based on the relation between the bulk expression level i and the regressor Typically, cell-type-specific effects of a trait on omics marker expression is analyzed by the linear regression in Eq. . For eacThe variable ollinear . We firsIn peripheral blood leukocyte data from a rheumatoid arthritis study GSE42861), the proportion of cell types ranged from 0.59 for neutrophils to 0.01 for eosinophils  were the most abundant Table . The proIn the above empirical data, multicollinearity between interaction terms seemed to arise not due to the correlation between cell type proportions or d in Eq. , the cormentclass2pt{minimmentclass2pt{minimBy using simulated data, we evaluated previous methods and new approaches of the omicwas package. In order to simultaneously analyze two scales, the linear scale for heterogeneous cell mixing and the log/logit scale for trait effects, we applied nonlinear regression [Eqs.  and 5)]]5)]. To Previous regression type methods are based either on the full model of linear regression [Eq. ] or the –7 for DNA methylation and false discovery rate < 5% for gene expression. In each simulation trial, the sensitivity, specificity and precision for detecting cell-type-specific association was calculated for each cell type. To compare algorithms, the performance measures for the same effect-size and cell type were averaged over the simulation trials.The simulation data was generated from real datasets of DNA methylation  and gene expression . The original cell type composition was retained for all samples, and the case–control status was randomly assigned. Ninety-five percent of omics markers were set to be unassociated with disease status, 2.5% were up-regulated in cases at one cell type, and 2.5% were similarly down-regulated. The cell-type-specific effect-size was fixed in a simulation trial, either to methylation odds ratio (OR) of 1.3, 1.6 or 1.9 or to gene expression fold change of 1.7, 3.0 or 5.0. The significance level was set to P < 2.4 × 10Overall, in the simulation for DNA methylation the sensitivity Fig.  was highThere was trade-off between sensitivity and precision. Among the algorithms, the Marginal model attained the highest sensitivity and the lowest precision. TCA, which is a variation of the marginal model, had relatively high sensitivity and relatively low precision. Marginal.Full005 attained the second highest sensitivity and moderate precision. The ridge regressions (omicwas.logit.ridge and omicwas.identity.ridge) attained moderate sensitivity and high precision. The full models without ridge regularization  had the lowest sensitivity.The overall tendency was similar in the simulation for gene expression. The sensitivity Fig.  was highThere again was trade-off between sensitivity and precision. Among the algorithms, the marginal models attained the highest sensitivity but relatively low precision. The nonlinear and ridge regressions and Marginal.Full005 attained moderate sensitivity and highest precision. The full models had very low average sensitivity of < 0.01.For gene expression, we also simulated a scenario where cell-type-specific disease effect occurred in cell type “marker” genes  the available datasets were underpowered to generate a gold standard list of differentially expressed omics markers . The nuh for a cell type, i for a sample, j for an omics marker (CpG site or gene), k for a trait that has cell-type-specific effects on marker expression, and l for a trait that has a uniform effect across cell types. The input data is given in four matrices. The matrix We begin by describing the linear regressions used in previous studies. Let the indexes be j, and omit the index for readability. For cell type h, the marker level of sample i isi,The parameters we estimate are the cell-type-specific trait effect full model of linear regression,h at a time by the marginal model,With regards to the statistical model, we assume the error of the marker level to be normally distributed with variance riations , 9, 13. riations , 11, 14.f = log for gene expression, and f = logit for methylation (see Background). Conventional linear regression can be formulated by defining f as the identity function. We denote the inverse function of f by g; g = exp for gene expression, and g = logistic for methylation. Thus, f converts from the linear scale to the normalized scale, and g does the opposite.Aiming to simultaneously analyze cell type composition in linear scale and differential expression/methylation in log/logit scale, we develop a nonlinear regression model. The differential analyses are performed after applying normalizing transformation. The normalizing function is the natural logarithm g, then averaged over cell types with weight f. Combined with the tissue-uniform trait effects, the mean normalized marker level in bulk tissue of sample i becomesThe marker level in a specific cell type [formula ] is moden is the number of samples and p is the number of parameters   \documen formula  reduces T indicates matrix transposition. The dot product of The parameters entclass1pt{minimak in Eq.  because ximation  as13\docU and V are orthogonal matrices, the columns of V are D are sorted Since our objective is to predict the cell-type-specific trait effects, we choose the regularization parameter based on . To simpformulae  and 14)\documentm in the summation for MSE in Eq. \documentCompute ridge estimators  formula  under \dApproximate the variance of ridge estimator, according to formula , by\docut-type test  are theEWAS datasets for rheumatoid arthritis were downloaded from the Gene Expression Omnibus. Using the RnBeads package (version 2.2.0)  of R, IDThe cell type composition of bulk samples was imputed using the Houseman algorithm  in the G+ monocyte data for 63 cases and 31 controls [+ B cell data for 108 cases and 95 controls. Under the nominal significance level P < 0.05 (two-sided), the number of CpG sites up- or down-regulated in cases were 20,869 (5%) and 14,911 (3%), respectively, in CD14+ monocyte and 28,004 (6%) and 26,582 (6%) in CD19+ B cell.Dataset GSE131989 included sorted CD14controls . By metacontrols , we obta+ monocyte data for 1200 samples and sorted CD4+ T cell data for 214 samples. Under the nominal significance level P < 0.05 (two-sided), the number of CpG sites up- or down-regulated by higher age were 45,283 (10%) and 80,871 (18%), respectively, in CD14+ monocyte and 35,822 (8%) and 25,020 (5%) in CD4+ T cell.From the Gene Expression Omnibus dataset GSE56047 , we obtaWhole blood RNA-seq data of GTEx v7 was downloaded from the GTEx website . Genes oThe cell type composition of bulk samples was imputed using the DeconCell package (version 0.1.0)  of R. Th+ monocyte data for 1202 samples and sorted CD4+ T cell data for 214 samples. Under the nominal significance level P < 0.05 (two-sided), the number of genes up- or down-regulated by higher age were 2715 (11%) and 3240 (13%), respectively, in CD14+ monocyte and 1082 (4%) and 1246 (5%) in CD4+ T cell.From the Gene Expression Omnibus dataset GSE56047 , we obtaBulk tissue sample data for case–control comparison were simulated based on the above-mentioned EWAS dataset GSE42861 and GTEx gene expression dataset. We randomly assigned the case–control status to the samples. Among the omics markers, 2.5% were set to be up-regulated in cases in single cell type, 2.5% were similarly down-regulated, and 95% were unrelated to case–control status. The cell-type-specific effect-size of the differentially expressed markers was fixed within a simulation trial, and was chosen from methylation OR of 1.3, 1.6 or 1.9 for EWAS  and foldrol (see ).For each effect-size, we performed 50 simulation trials. In each simulation trial, we randomly assigned half of the samples as cases  in all cell types other than the target cell type f = log, logit, identity without ridge regularization  or under ridge regression . The omicwas package was used also for conventional linear regression under the full and marginal models. We also developed a hybrid of marginal and full models : if the effect direction agreed in two models and if P < 0.05 in the full model, we adopted the Z-score of the marginal model; otherwise, the Z-score was set to zero.Cell-type-specific effects of traits was statistically tested by using bulk tissue data as input. We applied the omicwas package with the normalizing function Among previous methods, we evaluated those that accept cell type composition as input and compute test statistics for cell-type-specific association. For DNA methylation data, we applied TOAST (version 1.2.0) , CellDMC–7, which accounts for the correlation among the probes on HumanMethylation450 array [For simulated data of EWAS dataset GSE42861, we adopted the significance level P < 2.4 × 1050 array . For the50 array .h, we counted the four possible outcomes, true positives . In the same manner as the simulation analysis, we assessed the sensitivity, specificity and precision.Additional file 1: Table S1. Blood cell type proportion in Tsimane Amerindians, Caucasians and Hispanics.Additional file 2: Fig. S1. Sensitivity for detecting cell-type-specific association in simulated data for gene expression of marker genes.Additional file 3: Fig. S2. Specificity for detecting cell-type-specific association in simulated data for gene expression of marker genes.Additional file 4: Fig. S3. Precision  for detecting cell-type-specific association in simulated data for gene expression of marker genes.Additional file 5: Supplementary note. Asymptotic distribution of ridge estimator.


Lichtheimia corymbifera is considered as one of the most frequent agents of mucormycosis. The lack of efficient genetic manipulation tools hampers the characterization of the pathomechanisms and virulence factors of this opportunistic pathogenic fungus. Although such techniques have been described for certain species, the performance of targeted mutagenesis and the construction of stable transformants have remained a great challenge in Mucorales fungi. In the present study, a plasmid-free CRISPR-Cas9 system was applied to carry out a targeted gene disruption in L. corymbifera. The described method is based on the non-homologous end-joining repair of the double-strand break caused by the Cas9 enzyme. Using this method, short, one-to-five nucleotide long-targeted deletions could be induced in the orotidine 5′-phosphate decarboxylase gene (pyrG) and, as a result, uracil auxotrophic strains were constructed. These strains are applicable as recipient strains in future gene manipulation studies. As we know, this is the first genetic modification of this clinically relevant fungus. Rhizopus, Lichtheimia, and Mucor [Rhizopus delemar  [Lichtheimia species, primarily L. corymbifera, are most often recovered from clinical samples, especially in European countries [Mucormycosis is an invasive opportunistic fungal infection caused by certain members of the filamentous fungal order Mucorales . This lind Mucor ,7,8. Aft oryzae) , the Licountries ,4,8.The frequency of systemic mucormycosis is increasing, mostly because of the broadening of the susceptible population ,8,10. SuRhizopus and Mucor species, various classical genetic transformation-based molecular manipulation methods have been developed and applied [Mucor circinelloides [Rhizopus delemar [L. corymbifera, which seriously hampers the efforts to characterize the gene functions and reveal the genetic and molecular background of the pathogenicity of this fungus. Therefore, a plasmid-free, in vitro CRISPR-Cas9 method has been adapted and tested to carry out targeted gene disruption in L. corymbifera in this study. Using this technique, the pyrG gene encoding the orotidine 5′-phosphate decarboxylase (OMP decarboxylase) has been disrupted and a uracil auxotrophic strain, which can be used as a recipient strain in further genetic manipulation studies, could be created.For  applied ,13. Receelloides ,15 and p delemar . HoweverL. corymbifera pyrG gene were designed and synthesized in vitro (L. corymbifera protoplasts were co-transformed directly with one of these gRNAs and the Cas9 nuclease.Two guide RNAs (gRNAs) targeting different positions in the in vitro . L. corypyrG gene had been disrupted, were selected for their resistance to 5-fluoroorotic acid (5-FOA). Cells expressing OMP decarboxylase convert 5-FOA to a toxic compound, 5-fluorouridine monophosphate and cannot grow if 5-FOA is added to their culture medium [pyrG gene, display 5-FOA resistance and are auxotrophic to uracil  and the deletion in the pyrG gene, complemented the uracil auxotrophy and caused sensitivity to 5-FOA in the transformants , sodium dodecyl sulfate (SDS), and Triton X-100 as cell-wall and membrane-perturbing compounds on the fungal growth was tested. In general, ompounds b.pyrG disruption mutants and the original strain to oxidative stress was also examined. Strains, in which the pyrG gene was disrupted, showed a tendency to slightly higher sensitivity to hydrogen peroxide (H2O2) than the parental strain, but this difference was not found to be significant  or the homology-directed repair (HDR) mechanisms of the cell. The latter requires the presence of a template DNA containing sequences homologous with the targeted site to drive the homologous recombination. NHEJ generally causes indel (insertion or deletion) mutations and can be used for targeted gene disruption. These indel mutations are generally short with lengths ranging from one to a few tens of nucleotides in various fungi ,18,22,25 delemar . Interest fungus .L. corymbifera strains were isolated, 10 of them harbored deletions at the targeted site as a consequence of the CRISPR-Cas9 mutagenesis. 5-FOA resistance can be used to select for mutants in the genes pyrF encoding orotate phosphoribosyl transferase and pyrG encoding OMP decarboxylase [pyrF gene, may have caused the six false-positive colonies.Although 16 5-FOA resistant boxylase ,16. Bothboxylase ,16. SuchL. corymbifera, another aim of the study is to construct a stable uracil auxotrophic mutant, which can be applied in further gene manipulation and genetic transformation studies as a recipient strain. Using this strain, the pyrG gene of L. corymbifera can be applied as a selectable marker. Uracil auxotrophy may cause decreased growth and pathogenicity, and several studies have demonstrated that the pyrimidine biosynthetic pathway can affect these processes [M. circinelloides caused decreased virulence in the alternative Galleria model. However, uracil or uridine availability and activity of the uptake mechanisms can modify these effects in the different species and strains [pyrG gene had a moderate effect on the viability of the mutants. It did not affect the morphology and the pathogenicity of the fungus and slightly decreased its growth at certain temperatures and under cell wall stress. H2O2 may induce biophysical and permeability changes in the cell membrane [2O2 sensitivity of the mutants only slightly differed from those of the parental strain. At the same time, complementation of the disrupted pyrG gene restored the original phenotype. These results indicate that the pyrG mutants can be used as recipient strains in gene manipulation and other functional genetic experiments.Besides demonstrating the applicability of the CRISPR-Cas9 methods for the genetic modification of rocesses ,27,28,29rocesses  reported strains . Therefomembrane , potentiL. corymbifera type-strain (CBS 429.75) was used in this study. For nucleic acid extractions, 106 sporangiospores were plated onto a solid minimal medium , 1.5 g (NH4)2SO4, 1.5 g sodium glutamate, and 20 g agar per liter) supplemented with uracil (0.5 mg/mL), if required. Fungal cultures were grown for 4 days at 37 °C. To examine the effect of the temperature on fungal growth, strains were cultivated on solid YNB supplemented with uracil at 30, 35, 37, and 40 °C, plating 104 spores onto the medium. To test the mitotic stability of the transformants, malt extract agar  was used as the complete, non-selective medium.The Lichtheimia corymbifera JMRC:FSU:9682 genome [https://mycocosm.jgi.doe.gov/Liccor1/Liccor1.home.html) were used. Two different protospacer sequences were designed to target the DNA cleavage in the pyrG gene (Lichtheimia corymbifera JMRC:FSU:9682 genome database ID: LCOR_02455.1) and named as LcpyrGcr1 and LcpyrGcr2. The sequences and positions of the corresponding regions in the genome are presented in pyrG gene was amplified by PCR using the Phusion Flash High-Fidelity PCR Master Mix kit  and the primer pair LcpyrGfw (5′-atgaacaccttcaagacatacag-3′) and LcpyrGrev (5′-ctactgcttttgcacacgttc-3′). The amplified fragment was then sequenced commercially by LGC Genomics .Genomic DNA was isolated using the ZR Fungal/Bacterial DNA MiniPrep  according to the manufacturer’s instructions. PCR products were isolated and concentrated using the Zymoclean Large Fragment DNS Recovery Kit  and DNA Clean & Concentrator-5 . To design the oligonucleotide sequences, sequence data available in the 2 genome  databaseL. corymbifera cells as described earlier for M. circinelloides [The polyethylene glycol (PEG)-mediated protoplast transformation was used to introduce one of the gRNAs and the Cas9 enzyme  simultaneously into the elloides . Ten μM 2O2 was examined in a 96-well microtiter plate assay. H2O2 solution  was dissolved in liquid YNB medium to prepare a 100-mM stock solution. Final concentrations of H2O2 in the wells ranged from 0 to 10 mM; chemicals were diluted with liquid YNB medium. Spore suspensions were also prepared in liquid YNB , and the final amount of the spores in the wells was set to 105. Plates were incubated for 48 h at 37 °C and the optical density of the fungal cultures was measured at 620 nm using a SPECTROstar Nano  microplate reader. The uninoculated medium was used as the background for the calibration, and fungal growth in the H2O2-free medium was considered as 100%; all experiments were performed in triplicates.The sensitivity of the fungal strains to H4/mL) were point-inoculated at the center of solid YNB media  containing 1 µL/mL Triton-X 100 , 0.04 mg/mL SDS  or 0.1 mg/mL CFW  as the tested stressors. The diameter of the colonies was measured daily after incubating the plates at 37 °C.Fungal spores (10L. corymbifera strain and one of the constructed mutants (CBS 429.75-pyrGcr1/1) was tested in D. melanogaster. Spore suspensions were prepared with sterile phosphate buffer saline  from 4-day-old cultures grown on YNB plates  at 37 °C. The final inoculum concentrations were adjusted to 1 × 107 spores/mL with PBS. Drosophila stocks were raised and kept following the infection on standard cornmeal agar medium at 25 °C. The Oregon R strain, originally obtained from the Bloomington stock center, was used as the wild-type throughout the experiments. Infection was performed by dipping a thin needle in a suspension of fungal conidia (107 conidia/mL) or PBS for the uninfected control, and, subsequently, the thorax of the anesthetized fly was pricked. Flies were counted at different points of time to monitor survival. Flies were moved into fresh vials every other day. Each experiment was performed with approximately 60 flies for each genotype. The results shown are representative of at least three independent experiments.Pathogenicity of the original CBS 429.75 pyrG gene with its own promoter and terminator sequences was amplified by PCR using the Phusion Flash High-Fidelity PCR Master Mix  and the primer pair LcpyrG1 (5′-gtaatagcaaggaccaccgagtga-3′) and LcpyrG2 (5′-gaacaattaagagccgttgaatcc-3′). The amplified fragment was ligated into the pJet1.2 cloning vector , arising the plasmid pLcpyrGcompl , which has the binding site on the plasmid, and the LcpyrGrev primer . P-values less than 0.05 were considered statistically significant.All measurements were performed in at least two technical and three biological replicates. Significance was calculated with paired L. corymbifera. Our results demonstrate that the CRISPR-Cas9 method based on NHEJ is applicable to induce short, targeted deletions in the genes of this fungus. Using the described technique, the pyrG gene encoding the OMP decarboxylase has been disrupted. The resulting uracil auxotrophic L. corymbifera strains can be applied as recipient strains in further genetic manipulation studies. As we know, this is the first targeted gene manipulation of this mucormycosis-causing fungus.A plasmid-free, in vitro CRISPR-Cas9 system was adopted to carry out targeted mutagenesis in


Cytokines are a class of small proteins that act as chemical messengers and play a significant role in essential cellular processes including immunity regulation, hematopoiesis, and inflammation. As one important family of cytokines, tumor necrosis factors have association with the regulation of a various biological processes such as proliferation and differentiation of cells, apoptosis, lipid metabolism, and coagulation. The implication of these cytokines can also be seen in various diseases such as insulin resistance, autoimmune diseases, and cancer. Considering the interdependence between this kind of cytokine and others, classifying tumor necrosis factors from other cytokines is a challenge for biological scientists.In this research, we employed a word embedding technique to create hybrid features which was proved to efficiently identify tumor necrosis factors given cytokine sequences. We segmented each protein sequence into protein words and created corresponding word embedding for each word. Then, word embedding-based vector for each sequence was created and input into machine learning classification models. When extracting feature sets, we not only diversified segmentation sizes of protein sequence but also conducted different combinations among split grams to find the best features which generated the optimal prediction. Furthermore, our methodology follows a well-defined procedure to build a reliable classification tool.With our proposed hybrid features, prediction models obtain more promising performance compared to seven prominent sequenced-based feature kinds. Results from 10 independent runs on the surveyed dataset show that on an average, our optimal models obtain an area under the curve of 0.984 and 0.998 on 5-fold cross-validation and independent test, respectively.These results show that biologists can use our model to identify tumor necrosis factors from other cytokines efficiently. Moreover, this study proves that natural language processing techniques can be applied reasonably to help biologists solve bioinformatics problems efficiently. Mycobacterium tuberculosis infection [Cytokines is a varied group of polypeptides, usually linked to inflammation and cell differentiation or death. Among major families of cytokines (interleukins (IL), interferons (IFNs), tumor necrosis factors (TNFs), chemokine and various growth factors, comprised of transforminggrowth factor b (TGF-b), fibroblast growth factor (FGF), heparin binding growth factor (HBGF) and neuron growth factor (NGF)) , tumor nnfection . Anothernfection . For theTo date, in bioinformatics, several research teams have built machine learning models to predict cytokines and achieved high performance –20. BaseFrom the interpretation of the genetic alphabet order, scientists have found that in terms of in composition, biological sequences, particularly protein sequences, are comparable to human language . A growiMotivated by remarkable achievements in NLP using word embeddings, in this study, we tried to use NLP technique for extracting features. We transferred the protein sequences into “protein sentences” comprised of composing biological words from which word vectors were created. Next, we trained the Fast Text model to created word embeddings on which final word embedding-based features were generated. Finally, we employed some advanced machine learning algorithms for classification.Many studies in bioinformatics show that –37 to bun = 3), the numbers of features were highest (from 1736 to 1915) compared to those with length 1, 2, 4, or 5. In this group, the numbers of features when the length of biological words is equal to 2 is the second highest (from 395 to 398). Therefore, when we combined between 2-g and 3-g features, the total number of hybrid features is the highest one (from 2131 to 2313).It is necessary to figure out the quality as well as the quantity of extracted features for the learning process. Therefore, we calculated the number of features of word embedding-based vectors that were input in our binary classifiers  and negative group (non-TNF sequences). The Table p-values are generally high which means that there are not much difference in amino acid compostion between TNF and non-TNF sequences. However, at the common criterion , six amino acids C, G, K, W, Y, and V shows significant frequency difference. In addition, from the Table p-values which means that they may contribute more for distinguishing positive and negative data. It is also noted from the Table p-values . The other tripeptides with lowest p-values are QDG, TFF and VYV.From the Table In this analysis, we would like to envisage the surveyed protein feature vectors, which have a high number of dimensions and are very hard to see in the raw form. We performed t-Distributed Stochastic Neighbor Embedding  (t-SNE),n = 3), the numbers of features are highest  for building all classifiers in this study). In both 5-fold cross-validation and independent test, we assessed the general performance of each experiment using the AUC scores. The outcomes with highlighted best values in bold are shown in Table , 85%, 0.86 on the independent data in terms of accuracy, specificity, sensitivity, and MCC, respectively, see Table We used SVM to evaluate the efficiency of our suggested features against 7 frequently used feature types i.e. amino acid composition (AAC), amino acid pair composition (DPC), position-specific scoring matrix (PSSM) features and the combination of these types. After a search for the ideal parameters, we conducted these experiments on both cross-validation and independent data. We found that with our suggested features, the SVM models reached the best performance  First, we built L, an ordered list containing all protein words from the training data. (We v to denote the number of words in L), (2) Second, we used v embedding vectors appended one after another to describe each data sample. In this representation, the ifunction . The useOur study is a binary classification problem. First, we employed a five-fold cross-validation technique to develop and evaluate our models during the training process. Next, we assessed the capacity of our models in predicting unseen data using the independent dataset. We used four widely-used metrics sensitivity (Sen), specificity (Spec), accuracy (Acc) and Matthews’s correlation coefficient (MCC)  to Additional file 1 Statistical test results. Table S1: F and F-critical values from F-test to test the null hypothesis that two datasets’ variances are equal in terms of single amino acid, dipeptide and tripeptide composition. Table S2: p-values from the unpaired student T-test on single amino acid composition on positive group (18 TNF sequences) and negative group (non-TNF sequences) with the assumption that the variance are equal. Table S3: Top 20 dipeptides with the lowest p-values from the unpaired student T-test on dipeptide composition on positive group (18 TNF sequences) and negative group (non-TNF sequences) with the assumption that the variance are equal. Table S4: Top 20 tripeptides with the lowest p-values from the unpaired student T-test on tripeptide composition on positive group (18 TNF sequences) and negative group (non-TNF sequences) with the assumption that the variance are equal.Additional file 2 Visualization of feature sets. Fig. S1a: Visualization corresponding to protein feature vectors comprised from 1-g embedding vectors with perplexity equal to 25. Fig. S1b: Visualization corresponding to protein feature vectors comprised from 1-g embedding vectors with perplexity equal to 50. Fig. S2a: Visualization corresponding to protein feature vectors comprised from 2-g embedding vectors with perplexity equal to 25. Fig. S2b: Visualization corresponding to protein feature vectors comprised from 2-g embedding vectors with perplexity equal to 50. Fig. S3a: Visualization corresponding to protein feature vectors comprised from 3-g embedding vectors with perplexity equal to 25. Fig. S3b: Visualization corresponding to protein feature vectors comprised from 3-g embedding vectors with perplexity equal to 50. Fig. S4a: Visualization corresponding to protein feature vectors comprised from 4-g embedding vectors with perplexity equal to 25. Fig. S4b: Visualization corresponding to protein feature vectors comprised from 4-g embedding vectors with perplexity equal to 50. Fig. S5a: Visualization corresponding to protein feature vectors comprised from 5-g embedding vectors with perplexity equal to 25. Fig. S5b: Visualization corresponding to protein feature vectors comprised from 5-g embedding vectors with perplexity equal to 50. Fig. S6a: Visualization corresponding to protein feature vectors comprised from 1-g and 2-g embedding combined vectors with perplexity equal to 25. Fig. S6b: Visualization corresponding to protein feature vectors comprised from 1-g and 2-g embedding combined vectors with perplexity equal to 50. Fig. S7a: Visualization corresponding to protein feature vectors comprised from 1-g and 3-g embedding combined vectors with perplexity equal to 25. Fig. S7b: Visualization corresponding to protein feature vectors comprised from the combination of 1-g and 3-g embedding combined vectors with perplexity equal to 50. Fig. S8a: Visualization corresponding to protein feature vectors comprised from 2-g and 3-g embedding combined vectors with perplexity equal to 25. Fig. S8b: Visualization corresponding to protein feature vectors comprised from the combination of 2-g and 3-g embedding combined vectors with perplexity equal to 50.Additional file 3 The details of the feature extraction method with examples.Additional file 4 Formulae of assessment metrics.Additional file 5 The surveyed dataset.


ObjectiveOur study aimed to assess the mental health outcomes and coping strategies among healthcare workers (HCWs) in an already over-burdened maternity ward and labour room during the coronavirus disease 2019 (COVID-19) pandemic.MethodsThis cross-sectional questionnaire survey was conducted using Google Forms , which included demographic characteristics, perceived stressors, and validated scales: the Depression, Anxiety and Stress Scale - 21 Items (DASS-21), Insomnia Severity Index, and the Brief Coping Orientation to Problems Experienced (Brief COPE) scale. The results were evaluated and compared among COVID-19 caregivers and other HCWs.ResultsA total of 184 participants were included in the study, out of which 112 (60.9%) were COVID-19 caregivers. Overall, HCWs managing COVID-19 patients experienced significantly higher levels of depression, anxiety, and stress. They often adopted an avoidant coping style . The results of binary logistic regression analysis revealed that living with family and perceiving multiple stressors appeared to be associated with increased risk of anxiety while being a COVID-19 caregiver and appeared to be a risk factor for stress. Avoidant coping was found to be associated with insomnia while approach coping was less associated with anxiety.The most prevalent stressor among HCWs at our institute was distancing from family and friends (62%) followed by fear of getting infected (51.1%). Compared to other HCWs, the stressors perceived in significantly higher proportion by COVID-19 caregivers included distancing from family and friends , scarcity of workforce , and dealing with non-cooperative patients .ConclusionWe would request the immediate attention of the concerned authorities to implement interventions to buffer the impact of COVID-19 in the already stressed-out maternity wards and labour rooms. The World Health Organization (WHO) declared the coronavirus disease 2019 (COVID-19) outbreak a pandemic on March 11, 2020, and there has been a significant rise in infections among both the general public and healthcare workers (HCWs) since then . The speStudy design This was a cross-sectional online questionnaire-based study.ParticipantsAll HCWs in the Department of Obstetrics and Gynaecology at our institute were included after obtaining consent. HCWs were defined as all the staff members directly involved in patient care, such as doctors and nurses, or those indirectly involved, such as attendants, helpers, laboratory technicians, or house-keeping staff. They were further divided into two groups:Group 1: COVID-19 CaregiversThis group included staff members who were directly involved in COVID-19 care, such as those working in the screening/triage area, COVID-19 obstetrics ward, COVID-19 intensive care unit (obstetrics beds only), COVID-19 operation theatre (OT), and COVID-19 labour rooms.Group 2: Other HCWsThis group included staff members who were not directly involved in COVID-19 care, such as those working in non-COVID-19 areas in the Department of Obstetrics and Gynaecology.The HCWs were randomly allocated COVID-19 and non-COVID-19 rotational duties, which were compulsory for all. However, it was ensured as per the COVID-19 guidelines that high-risk groups  were excluded from direct COVID-19 duties.Inclusion criteria1. Participants giving consent.2. Participants not diagnosed with any psychiatric illnesses currently and in the past.3. Participants not on any psychotropic medications.4. Participants involved in the care of obstetrical patients.5. Participants of group 1 must have completed at least one spell (14 days) of COVID-19 duty.Exclusion criteria1. Participants not giving consent.2. Participants diagnosed with any psychiatric illnesses currently or in the past.3. Participants on any psychotropic medications.4. Participants involved in the care of non-obstetrical patients.5. Participants having a language barrier (not well versed in Hindi/English).Sample sizeA time-bound cross-sectional research design was used for this study. All completely filled responses received from HCWs within 21 days of sending the questionnaire were included in the study, and groups were classified based on available responses.Study toolA semi-structured questionnaire with 66 questions under five sections was used. It took around 15 minutes to complete the survey. The first section contained nine questions regarding demographic details. The second section consisted of 21 questions from a free validated standardized tool: the Depression, Anxiety and Stress Scale - 21 Items (DASS-21) , to asseProcedureThe questionnaire survey was done using the online web tool . The link was circulated on official obstetrics and gynaecology WhatsApp  groups of HCWs at the study institute and was kept open for 21 days. Three reminders at intervals of six days were sent out for filling up the same. The questionnaire began with a section on informed consent where the respondent was given the option to opt out of the survey at any time. Confidentiality and anonymity of the respondents were ensured. All completely filled responses received within 21 days were included in the study and analyzed. The results among COVID-19 caregivers and other HCWs were compared with respect to the magnitude and type of mental health outcomes, perceived stressors, and coping strategies being implemented on individual levels.Data managementThe data was imported to a Microsft Excel  spreadsheet, and the analysis was done using SPSS Statistics version 21.0 . Categorical variables were presented in numbers and percentages (%), and continuous variables were presented as means ± SD and median with interquartile ranges. The normality of data was tested with the Kolmogorov-Smirnov test. If the normality was rejected, then a non-parametric test was used. Statistical tests were applied as follows:1. Quantitative variables were compared using the Mann-Whitney test/Kruskal-Wallis test  between the groups.2. Qualitative and categorical variables were compared using the chi-square test.3. Binary logistic regression (stepwise backward LR) was used to determine the adjusted estimates.A p-value of <0.05 was considered statistically significant.A total of 184 HCWs responded to the survey and filled the questionnaire. The response rate was 71.8% (184/256).DemographicsThe demographic characteristics of the respondents are summarized in Table Mental health outcomesThe mental health outcomes of the respondents are summarized in Table Perceived stressorsCompared to other HCWs, COVID-19 caregivers perceived the following stressors significantly: distancing from family and friends , scarcity of workforce  and dealing with non-cooperative patients . Other stressors like lack of support from staff and administration, family and personal problems, and ethical dilemmas due to multiple new guidelines and information coming in every day were also perceived more by COVID-19 caregivers though the difference was not significant. Furthermore, COVID-19 caregivers experienced multiple stressors (>4) more often as compared to other HCWs   and the difference was also statistically significant with respect to avoidant subscales: behavioral disengagement , denial , substance use , and venting . Although there was no significant difference in approach coping scores between the two groups, informational coping was significantly higher among the COVID-19 caregivers . Also, humour approach scores were higher among COVID-19 caregivers  : 2.761, 95% CI: 1.028-7.413, p-value: 0.044; aOR: 1.355, 95% CI: 1.173-1.565, p-value: 0.000 respectively]. Again, being a COVID-19 caregiver appeared to be a risk factor for stress  while being married and being a doctor were associated with lower risk of stress . Furthermore, depression, stress, and anxiety appeared to be interrelated, each being a risk factor for others.As far as coping strategies were concerned, avoidant coping was found to be associated with insomnia  while approach coping was associated with depression and stress . Also, approach coping appeared to be less associated with anxiety  , nurses, and COVID-19 caregivers had significantly higher avoidant coping scores compared to non-COVID-19 HCWs. Emre Umucu et al. have demonstrated a positive association of COVID-19-related perceived stress with coping strategies: denial, substance use, behavioural disengagement, venting, religion, and self-blame. Our study revealed similar findings .Studies have reported that the kind of coping style can determine the occurrence of psychological distress. Practising a negative coping style leads to substandard mental health with long-term consequences . Koinis Strengths of the study1.This comprehensive evaluation of mental health was conducted in an already busy maternity ward with a very good response rate.2. The coping mechanisms were also studied in detail so as to conduct workshops accordingly to help our dedicated COVID-19 staff in maintaining sound mental health.3. The HCWs who screened positive for psychological issues were referred to the Department of Psychiatry for further management.Limitations of the study1. Only one institute was included in the study, resulting in a modest sample size and thereby limiting the generalizability of the findings.2. This was a cross-sectional study, and we did not follow up on the findings.COVID-19 HCWs in the Department of Obstetrics and Gynaecology at our institute have particularly shown increased susceptibility to depression, anxiety, stress, and insomnia. They are primarily using avoidant coping mechanisms. It is possible that COVID-19 would have a long-lasting effect on the psychology of HCWs in already over-burdened maternity wards. Since the mental health of HCWs in India is already a neglected area, the evidence of additional stress of pandemic as revealed in our study warrants immediate interventions like reducing the stressors, providing psychological care, social support, and developing healthy coping mechanisms. At our institute, the Department of Psychiatry has initiated many counselling sessions where priority is being given to COVID-19 caregivers involved in emergency services.


BackgroundIn degenerative brain diseases like Parkinson's disease (PD), alpha-synuclein (a-syn) can be in its monomeric (a-syn-mono) or toxic oligomeric (a-syn-oligo) or as a total  forms in the biological body fluids including saliva. Past research has observed major a-syn plasma variations in children with autism spectrum disorder (ASD) pointing toward brain degenerative components in their pathophysiology. No prior study has shown a-syn levels in ASD patients' saliva.Objective This study estimates the levels of alpha-synuclein monomer (a-syn-mono) and alpha-synuclein oligomer (a-syn-oligo) in the saliva of ASD affected children so that saliva can be a method for detecting disorder.Materials and methodsThis cross-sectional, multi-center study was conducted in Islamic International Medical College, Autism Resource Centre (ARC), and Step-to-learn Rehabilitation center for the slow learner in Rawalpindi. The research was performed for one year from August 2018 to August 2019. Saliva samples from 80 children  were collected. Specific anti-alpha-synuclein monomers (anti-a-syn-mono) and anti-alpha-synuclein oligomers (anti-a-syn-oligo) enzyme-linked immunosorbent assay (ELISA) kits analyzed the salivary samples. Mean ± SD were reported for quantitative data. The data between the two groups were compared using an independent t-test. The p-value of ≤ 0.05 was considered statistically significant.ResultsA total of 80 children were included in the study . The age of participating children was between four and eight years. The mean alpha-synuclein monomer level in the saliva of ASD children was 92.03 ± 117.09 pg/ml (p≤0.05), and in healthy subjects was 186.78 ± 239.31 ρg/ml. The levels of alpha-synuclein oligomer in the saliva of patients with ASD children were 0.13 ± 0.05 ng/ml (p<0.001), and in the healthy subjects was 0.33 ± 0.26 ng/ml. Both alpha-synuclein monomer and alpha-synuclein oligomer levels were low in the saliva of ASD children.ConclusionChildren with ASD had low levels of alpha-synuclein monomer and oligomer than healthy children which are unique than that of levels found in other degenerative brain diseases. Autism spectrum disorder (ASD) is a group of disorders that involve brain development in a way that the patients, in their early childhood development, develop two significant behavioral symptoms including social skill problems and restricted, stereotyped behavior. These symptoms appear before the age of three years, and most of these continue for the entire life . The speASD has a prevalence of between 1% and 1.5% globally . ASD is The human synuclein protein family comprises a small ~14kDa), natively unfolded alpha, beta, and gamma synuclein sub-classes. Alpha and beta types are abundant in the central nervous system and presynaptic nerve terminals, whereas gamma type expressed mainly in the peripheral nervous system . Alpha-skDa, natiThe diagnosis of ASD is clinical and primarily based on the history, examination, and observations of behavior with the help of scale based questionnaires like childhood autism rating scale-second edition (CARS-2) , and diaThis is a multi-center study conducted in the department of physiology and multidisciplinary research laboratory of Islamic International Medical College (IIMC), Rawalpindi, in association with the Autism Resource Center (ARC) and Step-to-learn Rehabilitation Center for the slow learners and special children, Rawalpindi. It was a cross-sectional study, and the period of the study was 12 months from August 2018 to August 2019. The ethical review committee of IIMC approved the study. Eighty children  were included in this study. Diagnosed cases of ASD and healthy children between three to eight years of age were included in the study. Children having known genetic or chronic illness were excluded from the study.Sampling techniqueA non-probability convenient sampling technique used to collect the required samples. After explaining the procedure and purpose, and obtaining written consent from the parents, the samples were collected. The salivary sample collection was according to the Devic et al.  protocol1. A sample of 2-3 ml of saliva from each ASD patient and healthy subject with 60 minutes fasting was collected by using a small sterilized folded cotton pad and encouraging ASD children to chew it by imitating chewing movements in front of them with the cooperation of their parents/the trainers. Giving enough time in the mouth to be wet. This cotton pad full of saliva was spit into a 50 ml sample collecting vial and then squeezed with gloved hands to get the salivary sample, which was immediately placed in an ice-box to decrease proteolytic activity.2. Samples centrifuged for 15 minutes at the 2,600 x g at 4°C to get the first clarification, and then again for another 15 minutes at 15,000 x g at 4°C to remove bacteria and contaminate from mucosal cells.3. After centrifugation, each sample received a protease activity-inhibiting buffer. The samples were then stored at -80°C until the enzyme-linked immunosorbent assay (ELISA) investigation.4. Monomeric and oligomeric alpha-synuclein levels in the saliva measured using relevant ELISA kits. Salivary a-syn-mono levels are in (pg/ml) and salivary a-syn-oligo levels in (ng/ml).Data analysisFor statistical analysis, we used the Statistical Package for the Social Sciences (SPSS), version 25 . Results were documented as mean ± SD. The independent samples t-test was applied to analyze the data between the two groups. A p-value of ≤ 0.05 was considered statistically significant.A total of 80 children included in the study, . The mean age of participating children in both groups was 5.78 ± 1.23 years, and all participants were between four to eight years of age. In ASD groups out of 40 patients, 35 were males and five females. The descriptive analysis showed that ASD is more prevalent in males as compared to females, with a ratio of 7:1 in the ASD group  and healthy subjects (186.78 ± 239.31 ρg/ml) having p-value ≤0.05  in the saliva of patients with ASD than the salivary levels of the alpha-synuclein oligomer 0.33 ± 0.26 ng/ml) in the healthy subjects having p-value <0.001 , and a-synuclein oligomers (a-syn-oligo), in the saliva of ASD children and uses them as a tool for a better understanding of pathophysiology and early diagnosis. Levels of a-syn-mono and a-syn-oligo assessed by analyzing their presence in the saliva of ASD children of age group four to eight years and comparing them with the healthy controls. The first finding was the reduction in a-syn-mono in the saliva of patients with ASD compared with healthy subjects. This finding is entirely in agreement with two previous observations done on the plasma of ASD children. A study by Kadak et al.  found a Although in ASD, the exact cause of the decrease in a-syn-mono level remains unclear as in PD a-syn polymerization, is demonstrated. This explanation suggests that a-syn may spread from cell bodies of salivary neurons via axons to the synaptic terminals around the epithelial cells of salivary glands to saliva. The reduced a-syn-mono concentration detected in the saliva of patients with PD/ASD may be because of an intracellular and axonal polymerization of a-syn in the neurons of the salivary nuclei or ganglia . Many stThe second finding is significantly decreased mean salivary level of a-syn-oligo in ASD patients than in healthy subjects. Although a-syn has never been tested in the saliva of ASD patients, this finding is surprising and contrary to the findings previously found in the saliva of PD patients  in whichIn vitro-generated a-syn-oligo induce transmembrane seeding of a-syn aggregation in neurons . Thus a-Currently, the diagnosis of ASD is based only on clinical assessment, and no biochemical test is available to speed up the diagnostic process. Early diagnosis and prompt behavioral therapy are the cornerstones to the successful management of ASD. The clinical variabilities in ASD children have encouraged increasing interest in biomarker detection as potentially useful indicators for diagnosis and treatment. Many studies identified potential blood-based biomarkers for ASD, including brain-derived neurotrophic factor (BDNF) , neurotrThere were few limitations like small sample size and synuclein levels not tested in the blood simultaneously to the study performed under the existing resources and facilities. Further elucidation about the alpha-synuclein level needs to be investigated, on a larger scale and in a different study setting, to confirm these findings and compare them against the severity of the ASD symptomatically.This study concluded that the children with ASD have low levels of both a-syn-mono and a-syn-oligo than healthy children and establishes the importance of saliva as an easily collectible and non-invasive diagnostic tool than blood and CSF in detecting alpha-synuclein in ASD. Measured together, salivary a-syn-mono and oligomer levels are appropriate biochemical markers for early diagnosis of ASD.


Moreover, low-fat mayonnaise enriched with GS-based microcapsules loaded with fish oil and containing WPH as a film-forming material (M-GS) presented higher oxidative stability after one month of storage when compared to low-fat mayonnaise enriched with either a 5 wt% fish oil-in-water emulsion stabilized with WPH or neat fish oil. This was attributed to a higher protective effect of the carbohydrate wall once the microcapsules were incorporated into the mayonnaise matrix.The influence of the carbohydrate-based wall matrix  and the storage temperature (4 °C or 25 °C) on the oxidative stability of microencapsulated fish oil was studied. The microcapsules (ca. 13 wt% oil load) were produced by spray-drying emulsions stabilized with whey protein hydrolysate (WPH), achieving high encapsulation efficiencies (>97%). Both encapsulating materials showed an increase in the oxidation rate with the storage temperature. The GS-based microcapsules presented the highest oxidative stability regardless of the storage temperature with a peroxide value (PV) of 3.49 ± 0.25 meq O Food fortification with omega-3 polyunsaturated fatty acids (ω-3 PUFAs) has gained increased scientific and industrial interest in the last decades . This isNonetheless, additional stabilization techniques are required , since it has been demonstrated that the emulsification process and subsequent drying using air at high temperatures (170−200 °C) result in the initial oxidation of the oil ,9. BioacWhey protein hydrolysates (WPH) have been reported to exhibit antioxidant activity  ,13. MoreIn this regard, the aims of the present study were to investigate: (i) the effect of the carbohydrate-based wall material  and (ii) the storage temperature (4 °C or 25 °C) on the oxidative stability of spray-dried microcapsules loaded with fish oil and produced using WPH as a film-forming material. In addition, the feasibility of using spray-dried microcapsules containing WPH for fortifying a low-fat mayonnaise  with ω-3 PUFAs was assayed. For that purpose, the physical and oxidative stabilities of low-fat mayonnaise enriched either with microcapsules loaded with fish oil, a fish oil-in-water emulsion stabilized with WPH or neat fish oil were investigated during one month of storage.w/w): 7.0% myristic acid (C14:0), 16.7% palmitic acid (C16:0), 8.8% palmitoleic acid (C16:1n-7), 4.1% stearic acid (C18:0), 8.2% oleic acid (C18:1n-9), 19.3% eicosapentaenoic acid (C20:5n-3) and 16.1% docosahexaenoic acid (C22:6n-3). Peroxide value (PV) of the fish oil was measured as described in 2/kg oil. Tocopherol content (TC) of the fresh oil was measured as described in 2/kg oil. Stabilizer Grinsted FF 1149 was kindly donated by DuPont . The rest of the ingredients used in the production of the mayonnaises were purchased in the local market. The rest of reagents used for analysis were of analytical grade.Whey protein (34.6 wt% protein content) and maltodextrin (DE 21) were kindly provided by Abbott , while the glucose syrup  was kindly donated by Cargill Germany GmbH . Alcalase 2.4 L was purchased from Novozymes . Refined fish oil (Omega Oil 1812 TG Gold) was acquired from BASF Personal Care and Nutrition GmbH  and stored at −80 °C until use. The fatty acid composition of the fish oil was determined by gas chromatography (GC), as described in , and it w/w). The degree of hydrolysis was estimated with the pH-stat-method, as described by Camacho et al.  values observed for this sample. Contrary to other studies, which found that the fortified mayonnaise with microencapsulated fish oil was the one with a higher D value [3] value ,45, our 3] value ,45 are mAfter 28 days of storage, fortified mayonnaise samples retained their physical integrity, since no phase separation or creaming were observed. However, changes in the droplet size distribution occurred . Contrar−1 for the mayonnaise samples. It was observed that the M-GS presented a significantly higher apparent viscosity, followed by the M-EM and M-NFO mayonnaises. These results are in line with those previously reported by Miguel et al. [p > 0.05), contrary to the M-NFO sample. This may be explained by the presence of WPH in the M-EM and M-GS mayonnaises, the latter also containing GS. This fact resulted in an improved physical stability of the M-EM and M-GS samples, which was attributed to an increased viscosity [All fortified low-fat mayonnaise samples presented pseudoplastic behaviors see . Table 2l et al. , who attl et al. . The latiscosity .Overall, the GS-based enrichment system led to a fortified mayonnaise with a high physical stability caused by an increased viscosity.2/kg oil), despite the different degrees of initial oxidation of the enrichment systems: (i) NFO , (ii) fish oil-in-water emulsion stabilized with WPH  and (iii) fish oil-loaded glucose syrup microcapsules containing WPH as a film-forming material . This can be explained, because the majority of the oil present in the mayonnaise was SFO , which is more oxidatively stable than fish oil and may have masked the contribution of fish oil oxidation in the mayonnaise samples after production.p < 0.05) from those of the beginning of the storage time. Interestingly, at day 28, the mayonnaise fortified with NFO presented a significantly higher PV (15.22 ± 1.45 meq O2/kg oil) compared to M-GS and M-EM. It should be also noted that, although the final PV of M-GS was significantly higher than the PV of M-EM, a slight decrease in the PV of M-EM was observed after day 21. This indicates that the rate of hydroperoxide decomposition was higher compared to the rate of hydroperoxide formation after day 21 for M-EM.Over storage time, the PV course showed a lag phase of 1 week for the three enriched mayonnaise samples . However2/kg oil) when compared to low-fat mayonnaise enriched with NFO (PV = 2 meq O2/kg oil) after 28 days storage at 25 °C. This finding was attributed to the partial disintegration of the hydrophilic biopolymer-based wall matrix of the electrosprayed microcapsules during mayonnaise production, which led to the release of already oxidized fish oil. On the other hand, Miguel et al. [2/kg oil after 21 days of storage at 25 °C). In the latter work, the authors confirmed that the microencapsulates remained intact after mayonnaise production, which explained the enhanced oxidative stability of the fortified product. Taking both studies’ results into account leads us to speculate that, in the current study, the physical integrity of the encapsulating wall was not severely affected either by the fortified mayonnaise production process (high-speed blending to disperse the capsules) nor the food matrix (water-based emulsion). However, the latter requires further investigation.It is also noteworthy that our results are not aligned with those reported by Hermund et al. . These al et al.  producedp > 0.05)  indicates the formation of secondary oxidation products, principally 2-alkenals and 2,4-alkadienals, which arise as a consequence of decomposition of the primary oxidation products (hydroperoxides) . The ini > 0.05) .During storage, the AV increased progressively for all the samples until day 21, from which a sharp increase in only the M-EM sample occurred. These results are in line with those previously reported for PV since, after day 21, the hydroperoxide content of the M-EM sample decreased , suggestp > 0.05). However, M-GS showed a lag phase of one week, contrary to M-NFO  were produced via spray-drying using either GS or MD21 as wall materials and WPH as a film-forming material. GS-based microcapsules showed a higher oxidative stability when compared to MD21-based microcapsules at the two storage temperatures assayed (4 and 25 °C), which was mainly attributed to the lower oxygen diffusivity in GS-microcapsules. However, the higher oxidative stability for the GS-based microcapsules obtained, when compared to previous studies, also suggested a positive influence of the use of WPH as a film-forming material exhibiting antioxidant activity. Moreover, our results showed that the delivery system used to enrich low-fat mayonnaise played a major role on the oxidative stability of the fortified product. Adding the fish oil in the form of a fish oil-in-water emulsion stabilized with WPH-favored lipid oxidation in the enriched product when compared to the addition of either neat or microencapsulated fish oil. This may be explained by successive emulsification processes that contributed to dispersing oxygen and prooxidant agents within the matrix, as well as to increasing the interfacial area. Fortified mayonnaise with GS-based microcapsules loaded with fish oil and containing WPH as a film-forming material showed the higher oxidative stability after storage. This was mainly explained because the physical integrity of the microencapsulates may have remained intact after mayonnaise production. Thus, our results show the potential of using WPH as a film-forming material for the production of fish oil-loaded microcapsules to be used as omega-3 delivery systems.


Multiple myeloma (MM) is a plasma cell malignancy that is currently incurable. Finding new targets and designing drugs are crucial for the treatment of MM. The two datasets (GSE6691 and GSE39754) are used to screen highly expressed antigen on MM cells. HLA-E was an ideal target for it was a hub gene, and also located in one of the key clusters. Highly expression of HLA-E mRNA on MM cells was also confirmed by real-time qPCR testing the MM patients’ samples in Shengjing hospital. Crystal structure of HLA-E was obtained from Protein Data Bank (PDB ID: 3CDG) which was used to design targeting peptides with Molecular Operating Environment software. By analyzing interaction between CD94/NKG2A and HLA-E, a peptide with twelve amino acids was screened as a model peptide. Peptides library was constructed by randomly replaced non-key amino acid. Peptide-protein docking method was used to identify high affinity peptides. PEPTIDE 1-3 and model peptide were synthesized and identified the affinity to HLA-E by flow cytometer and confocal laser microscopy. At last, PEPTIDE3 (NALDEYCEDKNR) was found with the highest affinity. Taking all, HLA-E is a new treatment target, and PEPTIDE 3 is an ideal high affinity target-binding peptide candidate. Multiple myeloma (MM) is a common plasma cell malignancy that accounts for more than 17% of hematological malignancies and 1.8% of all cancers in the United States , 3. HoweMicroarray technology is widely used to investigate differential mRNA expression and identify biomarkers, which provides important clues to identify new treatment targets for many diseases, including MM , 5. In tTargeting therapy has achieved great improvements in the treatment for malignancies, including MM  . At the http://www.ncbi.nlm.nih.gov/geo) database with MM patients and normal plasma cell samples. The number of upregulated DEGs on MM in the GSE6691 and GSE 39754 datasets were 580 and 682, respectively. The overlap between the two datasets consisted of 134 upregulated DEGs  [http://www.cytoscape.org/, version 3.6.0) (The protein-protein interaction (PPI) network was constructed from the 134 co-upregulated DEGs though STRING (on 10.5)  and visun 3.6.0) . Five mon 3.6.0) –1E and, n 3.6.0) . The foun 3.6.0) .https://www.rcsb.org), HLA-E was the only key membrane protein suitable for designing anti-myeloma peptide drug-candidate. And HLA-E was in the cluster 3 and hub genes as shown in To design a peptide drug candidate for the treatment of MM, the suitable targets had to be membrane proteins. Thus, DDOST, HLA-E, and CANX were selected as potential targets. NME1 was excluded because it is an intracellular protein. However, a protein lacking available structural information could not be used as a target for the computational design of a peptide drug. Following a search of the RCSB Protein Data Bank  in Shengjing hospital. After sorted by CD138 beats, HLA-E mRNA was detected. There were significantly higher HLA-E expression levels in the MM samples compared with the controls (http://www.chemcomp.com/). After refining and energy minimization steps, the area of the HLA-E complex that interacts with CD94/NKG2A was identified. Subsequently, a detailed analysis of the binding sites was carried out to investigate the favorable ligand-receptor contact regions. Both CD94 and NKG2A could interact with the pocket of HLA-E. The model peptides were obtained from CD94 or NKG2A which could interact with the pocket of HLA-E. Before docking, peptides conformation searching was done to make the peptide more realistic. Finally, a peptide segment containing 12 amino acids obtained from CD94 which was more stable, could be used as a model peptide for binding more accurately to the pocket of HLA-E. Protein docking showed that the model peptide could interact with the pocket of the HLA-E complex   had been complex , Table 2 complex . Docked  complex , Table 2As previous report, only with leader peptide could HLA-E protein express on the surface of the cells . There wTM, Hangzhou, China). The result showed that 293T cell line didn’t express HLA-E protein on cell surface  were synthesized and labeled by FITC fluorescein. We built HLA-E high expressed cell lines 293T and U266 by plasmid transfection and leader peptide pretreating respectively. FCM and confocal laser microscopy were used to detect the FITC labeled HLA-E targeting peptides. The result verified the computer work, PEPTIDE 3 had the highest affinity to HLA-E. Indicating PEPTIDE 3 could be considered as a potential targeting peptide drug candidate to target MM cells.However, there are still some limitations in our study. We verified HLA-E high expression on some MM patients, while the number of patients was not large enough. Moreover, the prognostic significance of HLA-E on MM patients was not discussed. We also need to clarify the side effect of PEPTIDE 3, especially off-target effect in vivo experiment. In further, we will do the biological effects of PEPTIDE 3 not only on MM cells cell lines and in myeloma primary cells but also in vivo.In summary, the present study has identified HLA-E as a new therapeutic target for MM and designed affinity peptides targeting it. PEPTIDE 3 has the strongest affinity to HLA-E. In future studies, the affinity peptide could also be used to produce peptide-drug-conjugates (PDC) for the target treatment of MM. The present work provides a new way for the treatment of MM.GSE6691 and GSE39754 datasets were downloaded from the GEO database and combined for analysis. The datasets included bone marrow samples from 182 MM patients and 11 healthy volunteers (control samples).http://www.ncbi.nlm.nih.gov/geo/geo2r). Log2FC (fold change) > 1.5 and P-value <0.05 were set as standard criteria. The co-DEGs between GSE6691 and GSE39754 were identified by Venn diagram online (http://bioinformatics.psb.ugent.be/webtools/Venn/).The upregulated DEGs in MM samples compared to the controls were screened using GEO2R  .-ΔΔCt method was used to calculate the fold-change of gene expression.Total RNA was extracted using Trizol . Reverse transcription  was performed using a cDNA synthesis kit . qRT-PCR was performed with SYBR Premix EX Taq  using the QuantStudio 3 real-time PCR system . RT-qPCR primers were synthesized as described : HLA-E f2O molecules, and energy minimization. Next, we identified the key interacting area between HLA-E and CD94/NKG2A by analyzing the bonding and mining literature data. A peptide obtained from CD94/NKG2A was used as a model peptide for interaction with the HLA-E pocket. The peptide library was constructed based on the model peptide using the residue scan module in MOE software for random replacement of non-key amino acids. All peptides with predicted high affinity for HLA-E were optimized as follow. Firstly, conformational searching of these peptides was conducted with LowModeMD method; Secondly, docking was conducted based on two steps (placement and refinement) of Dock module in MOE. Triangle matcher and rigid receptor method were used in these two steps. 100 poses would produce in the placement step and at last exported 10 poses. At last, London dG and GBVI/WSA dG scoring function were used to estimate the affinity of peptide to HLA-E. The binding affinity of these peptides could be predicted by forming interaction bonds and binding free energy .Crystal structure of homo sapiens’ HLA-E complex interacting with CD94/NKG2A was obtained from the PDB website. The result showed that there were two crystal structures available listed as PDB code 3CDG and 3CII. 3CDG with the higher resolution which would be used as the foundation for the design of affinity targeting peptides. Interactions between proteins and peptide-protein docking were performed by MOE software. To design affinity peptides targeting HLA-E, we firstly fulfilled the structure preparation of the HLA-E-CD94/NKG2A complex by addition of hydrogen, removal of HTM, Hangzhou, China). The HLA-E antibodies were purchased from Biolegend . 5μg/ml, 10μg/ml, 25μg/ml, 50μg/ml model and PEPTIDE 1-3 were cultured with HLA-E high expressed 293T cells and 293T cells. Then affinity of peptides against HLA-E were detected by FCM. Firstly, a ‘live gate’ was circled. Secondly, Cells were first gated by their FSC/SSC properties, and then the percentage of each concentration FITC-labeled peptides were computed (293T cells as a tool cell line which collected by our laboratory were used for construction HLA-E high express cell lines by transfecting HLA-E plasmid with Lipofectamine 3000 (Invitrogen) as previous report . Affinitcomputed .U266 cells was a common kind of myeloma cell lines which could be inducted to express HLA-E by adding leader peptide as previous report . U266 ce5) were mixed into a six-hole plate for 1h and then fixed by polyformaldehyde for 30mins. Membrane and nuclei were dyed by DLI and DAPI for 30mins after washing and resuspending the cells with phosphate buffer solution (PBS). Nikon C2 confocal microscope was used for microscopic observations [HLA-E high expressed U266 cells and U266 cells were used for verification the affinity of the targeting peptides against HLA-E by confocal laser microscopy . Prepararvations . The samt-test (SPSS software 23.0). A P-value < 0.05 was considered statistically significant.The data were expressed as the mean ± SD and two group statistical comparisons of means were calculated using the Student's Supplementary Figure 1


Heart failure (HF) is the terminal stage of all cardiovascular events. Although implantable cardioverter defibrillator (ICD) therapies have reduced mortality among the high-risk HF population, it is necessary to determine whether certain factors can predict mortality even after cardiac device implantation. Growth stimulation expressed gene 2 (ST2) is an emerging biomarker for HF patient stratification in different clinical settings.  This study aimed to investigate the relationship between baseline soluble ST2 (sST2) levels in serum and the clinical outcomes of high-risk HF patients with device implantation.  Between January 2017 and August 2018, we prospectively recruited consecutive patients implanted with an ICD for heart failure, with LVEF ≤35% as recommended, and analyzed the basic characteristics, baseline serum sST2, and NT-proBNP levels, with at least 1-year follow-up. All-cause mortality was the primary endpoint. vs. 5.33%, P = 0.034). After adjusting the model  and the model combined with NT-proBNP, the risk of all-cause death was increased by 2.5% and 1.9%, respectively, per ng/ml of sST2. The best sST2 cutoff for predicting all-cause death was 43.42671 ng/ml . Compared to patients with sST2 levels below 43.42671 ng/ml, the risk of all-cause mortality was higher in those with values above the threshold . ST2 level ≥43.42671 ng/ml was an independent predictor of all-cause mortality (HR: 3.30 [95% CI 1.02–10.67]). Age (HR: 1.06 [95% CI: 1.01–1.12]) and increased NT-proBNP per 100 (HR: 1.02 [95% CI: 1.01–1.03]) were also associated with all-cause mortality in ICD patients.  During a 643-day follow-up, all-cause mortality occurred in 16 of 150 patients (10.67%). Incidence of all-cause mortality increased significantly in patients with sST2 levels above 34.98846 ng/ml (16.00%  sST2 level was associated with risk of all-cause mortality, and a threshold of 43.43 ng/ml showed good distinguishing performance to predict all-cause mortality in patients with severe heart failure, recommended for ICD implantation. Patients with sST2 levels more than 43.42671 ng/ml even after ICD implantation should therefore be monitored carefully. Heart failure (HF) is the terminal stage of all cardiovascular events and is a major cause of mortality in developing and developed countries , 2. AlthRecently, growth stimulation expressed gene 2 (ST2) is a novel emerging biomarker for patient stratification in different clinical settings –14. ST2 This study aimed to investigate the relationship between baseline sST2 levels in serum and the clinical outcomes of high-risk HF patients after ICD implantation and to find a suitable sST2 level that could evaluate the mortality risk of HF patients even after ICD therapy.Between January 2017 and August 2018, patients with chronic heart failure were prospectively enrolled in our study at the cardiology department of the General Hospital of Northern Theater Command. Patients aged more than 18 years, with LVEF ≤35%, and recommended and implanted with an ICD as per the guidelines, were considered eligible. The exclusion criteria were age <18 years, acute coronary syndrome, severe heart valve disease, severe liver and kidney disease, severe anemia, end-stage chronic obstructive pulmonary disease, acute asthma attack, trauma or surgery of any type within the preceding 8 weeks, mental disorders, rheumatoid arthritis, and systemic lupus erythematosus. All the patients were followed up for at least 1 year; the final follow-up point was September 30, 2019. All-cause mortality was the primary endpoint of this study. Patient management was at the discretion of the treating physician and was provided in accordance with the recommended guidelines.Blood was drawn from a forearm vein and was collected in an ethylenediaminetetraacetic acid (EDTA) tube before device implantation when patients' heart function was relatively stable. Samples were centrifuged, and plasma was frozen within 1 h of sampling at −80°C for pending analysis.Serum ST2, the soluble form of ST2 (sST2), a novel marker of myocardial stretchNT-proBNP, an established marker of myocardial stretchWe analyzed two plasma biomarkers that reflect a range of pathophysiological processes in CHF:These biomarkers were chosen based on previous studies in patients with CHF that demonstrated their independent association with mortality and SCD.Serum samples and plasma samples were transported under controlled conditions to a central laboratory for batch analysis of sST2 and NT-proBNP levels. sST2 concentrations were determined in serum by single measurement using a quantitative sandwich monoclonal enzyme-linked immunosorbent assay . In our hands, the average intra-assay coefficient of variation was 5.0%, in line with the average interassay coefficient of variation of 5.2%, as reported by the manufacturer. NT-proBNP concentrations were determined in plasma using the Elecsys NT-proBNP electrochemiluminescent sandwich immunoassay on a Cobas 8000 analyzer . Analysts were blinded to the patients' characteristics and endpoints.Treatment for heart failure with evidence-based medical therapies such as beta-blockers, angiotensin-converting enzyme inhibitors (ACEIs) or angiotensin receptor blockers (ARBs), and aldosterone receptor antagonists was optimized in all patients. Device programming was at the discretion of the treating physician. Following enrollment, patients were followed up for 2–4 days on a monthly basis, with a hospital visit or via a remote patient management system. Patients under remote follow-up also attended the hospital every 3 months. At each follow-up, the device was interrogated. The occurrence of any ICD and CRT-D therapy was recorded, and survival was recorded. Clinical information regarding all-cause mortality and major adverse cardiac events (MACE) including cardiac death and requirement for heart transplantation was gathered during a follow-up of 643 days after enrollment. The information was collected by the treating cardiologists who were unaware of the patients' biomarker levels. The cause of death was ascertained by reviewing medical records and contacting the patients/family members. Causes of death were categorized according to a modified Hinkle–Thaler classification .The study complied with the Declaration of Helsinki and was approved by the local research ethics committee. Written informed consent was obtained from all patients.t-test, and non-normal distribution data were compared using the Wilcoxon rank sum test. Categorical variables were compared using the chi-square test or Fisher's exact test. Cumulative event rates for clinical outcomes between groups were calculated based on Kaplan–Meier estimates. Survival curves for time-to-event variables were compared using the log-rank test. A receiver operating characteristic curve for serum sST2 was constructed to assess its predictive accuracy for all-cause mortality. The area under the curve for SRI was computed to identify the Youden index (best cutoff) for all-cause death. Multivariable Cox proportional hazards models were constructed to identify independent predictors of all-cause mortality. The variables in the multivariable analysis included age, gender, device implantation, prevention of sudden death, left ventricular end-diastolic diameter (LVEDD), left ventricular ejection fraction (LVEF), white blood cell (WBC), left bundle branch block (LBBB), etiology, estimated glomerular filtration rate (eGFR), and N-terminal probrain natriuretic peptide (NT-proBNP). Statistical analyses were performed using SPSS version 22.0 , and P values <0.05 were considered to indicate statistically significant differences.Continuous variables are presented as mean ± SD or medians (interquartile ranges [IQR]), and categorical variables are presented as counts and percentages. Normal distribution continuous variables were compared by Student's At the baseline, 150 patients were available for the analysis of sST2. Of these, 113 patients (75.33%) were men, and the mean age was 61.63 ± 11.14 years (range 25–86 years). Before device placement, basic LVEF was 29.41 ± 4.69% ranging from 19% to 35%. Of all patients, most were in the New York Heart Association functional class (NYHA FC) III and IV . In all, 80 (53.33%) patients were implanted with ICD , and the other 70 (46.67%) patients were implanted with CRT-D. Of all patients, 70 (46.67%) patients were diagnosed with ischemic heart failure, 110 (73.33%) with primary prevention for sudden cardiac death, and 62 (41.33%) with complete left bundle branch block (CLBBB). The average sST2 concentration was 41.22 ± 24.61 ng/ml with levels ranging from 12.81 to 204.78 ng/ml. Overall, we divided the population into two groups according to the median sST2 level (ST2 ≥34.9885). Baseline characteristics for both groups are shown in P = 0.0046). Patients who died were older and had significantly higher levels of hsTNT and NT-proBNP and lower levels of eGFR than patients who survived during follow-up (Table S1).During a 643-day follow-up , all-cause mortality occurred in 16 patients (10.67%) among the 150 patients. The cause of death was classified as cardiovascular death in 14 patients (9.33%). Among the 14 patients, 9 patients (64.28%) died of terminal heart failure, and 5 patients (35.72%) died of arrhythmia or sudden cardiac death. The median level of sST2 concentration was 45.98 (35.09–67.29) in the patients who died, whereas it was 33.80 (25.72–45.43) in the surviving group(P = 0.034)  . When co= 0.034) .P = 0.002) . This cutoff allowed good risk stratification in the Kaplan–Meier analysis. Compared with patients showing sST2 levels below 43.42671 ng/ml, the risk of all-cause mortality was higher in the patients with levels above the threshold  . Meanwhi= 0.002) .In the present study, we focused on patients with severe heart failure who were suitable and implanted with ICD to further decrease mortality from heart failure. In long-term observation after ICD therapy, we found that the levels of sST2 43.43 ng/ml could predict the all-cause mortality efficiently. NT-proBNP, sST2, and age have also been described as independent predictors for all-cause mortality in HF with ICD.Our results are consistent with the study by Hicham et al. reporting that sST2 and NT-proBNP are promising biomarkers for identifying patients with little potential to gain a significant survival benefit from ICD therapy . Both thP = 0.002) among heart failure patients with ICD. The results imply that the baseline sST2 has significant prognostic value in HF patients with ICD and that patients with sST2 levels greater than 43.42671 ng/ml should be monitored carefully even after ICD implantation.NPs are the major biomarkers used for heart failure including both BNP and NT-proBNP, which have become a part of routine care for HF diagnoses in both acute and ambulatory settings. Although the prognostic value of NPs is well established, their adoption by clinicians has been irregular because NT-proBNP is affected by many factors such as gender, age, BMI, and renal function –11, 29. Overall, we explored the predictive effect of sST2 on all-cause mortality in patients with heart failure and EF <35% and implanted with cardiac devices. For the first time, we proposed the threshold of sST2 ≥43.42671 ng/ml as a good distinguishing cutoff value to predict all-cause mortality.Based on the above research results, we will design a large-sample, multicenter study to further determine the predictive effect of sST2 >43 in all-cause mortality in patients with EF <35% ICD implantation in the next study. Moreover, for patients with sST2 >43 obtained in this study, we will further give more intensive medical therapies that can improve the short-term or long-term prognosis of patients.First, our study sample is small, and the number of deaths is low. The results were derived from a single-center study with a small patient cohort, and the enrollment period was only 20 months. This is probably because all the patients were eligible and were implanted with a cardiac device. Larger cohorts are required to assess the sensitivity and specificity of prognostic performance measurements. Secondly, the follow-up was not long enough. All patients were followed up for at least 1 year; the longest follow-up was more than 2 years. In all, 100% of real-world ICD patients were followed up in this study. Only 16 patients died in our study among 150 HF patients with ICD, and the death rate in our study (10.67%) was similar to the 1-year mortality rate in the ICD arm of MADIT-II (9%). Third, although all patients had received adequate management based on guideline-recommended therapy, the patient management was at the discretion for the different treating physician which still was a limitation. Fourth, this study did not include an in-depth analysis of pathophysiological mechanisms and signaling pathways.The level of sST2 was found to be associated with the risk of all-cause mortality. The sST2 threshold of 43.43 ng/ml had a good discrimination performance to predict all-cause mortality in severe heart failure patients recommended for ICD implantation.


The purpose is to assess the ability of low-dose CT (LDCT) to determine lung involvement in SARS-CoV-2 pneumonia and to describe a COVID19-LDCT severity score.Patients with SARS-CoV-2 infection confirmed by RT-PCR were retrospectively analysed. Clinical data, the National Early Warning Score (NEWS) and imaging features were recorded. Lung features included ground-glass opacities (GGO), areas of consolidation and crazy paving patterns. The COVID19-LDCT score was calculated by summing the score of each segment from 0 (no involvement) to 10 (severe impairment). Univariate analysis was performed to explore predictive factor of high COVID19-LDCT score. The nonparametric Mann-Whitney test was used to compare groups and a Spearman correlation used with p<0.05 for significance.Eighty patients with positive RT-PCR were analysed. The mean age was 55 years ± 16, with 42 males (53%). The most frequent symptoms were fever  and cough , the mean NEWS was 1.7±2.3. All LDCT could be analysed and 23/80 (28%) were normal. The major imaging finding was GGOs in 56 cases (67%). The COVID19-LDCT score  was correlated with NEWS . No symptoms were risk factor to have pulmonary involvement. Univariate analysis shown that dyspnea, high respiratory rate, hypertension and diabetes are associated to a COVID19-LDCT score superior to 50.COVID19-LDCT score did correlate with NEWS. It was significantly different in the clinical low-risk and high-risk groups. Further work is needed to validate the COVID19-LDCT score against patient prognosis. The outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), that began in Wuhan, China, spread worldwide, and is now in the ascending phase of the epidemic in Europe . SARS-CoThe main problem of COVID-19 pneumonia is the risk of saturation of our health system due to an uncommonly massive inflow of patients requiring intensive care. Patient selection is therefore essential in order for health care practitioners to focus on patients with comorbidities and on severe cases. This selection is currently essentially based on clinical criteria.Reverse-transcription polymerase chain reaction (RT-PCR) is currently the standard of reference for diagnosis of COVID 19 pneumonia. Chest x-ray might be replaced by low-dose computed tomography (LDCT) in COVID-19 to assess lung involvement. Thus, computed tomography (CT) has an important role in the management of patients, both for early screening and diagnosis and for establishing disease severity –5.COVID-19 pneumonia lung lesions are now well characterized: they are typically asymmetric, with ground-glass opacity (GGO) lesions or, less commonly, pulmonary areas of consolidation, they have a peripheral distribution, and they preferentially affect the lower territories . It seemThe objective is to evaluate the ability of (LDCT) to analyze well-known imaging abnormalities as well as to establish a COVID19-LDCT score reflecting disease severity and correlate it with clinical risk scores to allow better selection and follow-up of patients.th to the 20th of March 2020. The inclusion criteria and patient enrolment included all consecutive patients presenting to the department of infectious disease for 12 consecutive days with a diagnosis of COVID-19 confirmed by RT-PCR. All patients underwent LDCT. The protocol was approved by the local institutional review board: institut hospitalo-universitaire méditéranée-infection N°:2020–0012. The exclusion criteria was protocol refusal. For each patient, the following clinical parameters were recorded: age, sex, date of first symptoms, date of chest CT scan, delay between the first symptom and chest CT scan, fever, cough, dyspnea, diarrhea, myalgia, rhinorrhea, abnormalities at lung auscultation, temperature, heart rate, blood pressure, respiratory rate, oxygen saturation, and oxygen needed. Medical history parameters were recorded as follows: heart disease, tobacco use, COPD, asthma, diabetes, obesity, sleep apnea syndrome, oncologic status and immunosuppression status. The National Early Warning Score (NEWS) was calculated for each patient using respiratory rate, oxygen saturation, supplemental oxygen needed, temperature, systolic blood pressure, heart rate, and AVPU score, as described previously  and 96.9% ± 2.1 [91.0–100.0] respectively (p = 0.07). Dyspnea was found in 11/80 cases. Over 69 patients without dyspnea 47 have an abnormal LDCT and 22 were normal. Patients with normal LDCT showed a higher pulse oxymetry 98.0% ± 1.4 [95.0–100.0] compared to patient with pneumonia on LDCT 97.2% ± 1.8 [91.0–100.0] (p = 0.028) .Sixty-nine patients were classified as having low NEWS, and 11 patients had high NEWS. The COVID-LDCT score was 13 ± 19 in the low-NEWS group and was significantly higher in the high-NEWS group .There was no significant difference between groups according to their symptoms or blood pressure at the time of admittance. Patients with a NEWS > 4 had a significantly higher temperature, heart rate, and respiratory rate and a lower oxygen saturation than patients with a low NEWS . Oxygen was needed in 7 of 11 patients (64%) in the high-NEWS group and in 3 of 69 patients (7%) in the low-NEWS group . The cliThe main findings of the presented work are that 1) LDCT scans can depict the typical features of SARS-CoV-2 pneumonia with limited irradiation; and 2) the COVID19-LDCT score is correlated with the NEWS used routinely to assess disease severity and patient prognosis.Unenhanced chest CT was performed using a low-dose acquisition protocol. All the CT scans performed allowed optimal analysis of parenchyma involvement. Recently, recommendations have been made regarding the use of LDCT for COVID19 that indicate preference for LDCT . They reAs recently described, the clinico-radiological correlation is good, and diffuse lung involvement is frequently observed in severe forms , 21. LesThe COVID19-LDCT score may provide the quantitative measurement of COVID-19 injury. Patients can be easily classified in various degrees of the disease. This type of CT score was first described in 2004 for severe acute respiratory syndrome (SARS). Investigators divided each lung into 3 parts  and evaluated the involvement on a scale of 0–4: 0, <25%, 25–50 >50% and >75% involvement: the maximum possible score was 24 . The preThere are a few limitations in the present study. Our population was mainly paucisymptomatic patients which did not allow us to have a wide repartition of NEWS and CT scores for correlation, especially for high scores. This could be explained because we enrolled the 80 first patients with COVID-19 pneumonia in our centre and the severe forms are not the most frequent. Another limit is the low number of patient that does not allowed us to perform a multivariate analysis and further studies are needed to establish this score a clinical prognosis tool.Low-dose CT scan could depict the typical features of COVID-19 pneumonia. In this population of 80 COVID-19 patients detected positive by RT-PCR, there was a significant correlation between the clinical NEWS and the COVID19-LDCT score, showing the close relationship between signs and the extent of disease in the lung. More work is required to assess whether this score could add clinically relevant information to assess the prognosis of patients with COVID-19 pneumonia.S1 File(PDF)Click here for additional data file.S1 Data(XLSX)Click here for additional data file.